Overview

Dataset statistics

Number of variables28
Number of observations82
Missing cells14
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.1 KiB
Average record size in memory225.6 B

Variable types

Numeric9
Categorical19

Alerts

airdate has constant value "2020-12-21" Constant
_embedded_show_dvdCountry has constant value "nan" Constant
url has a high cardinality: 82 distinct values High cardinality
name has a high cardinality: 66 distinct values High cardinality
_embedded_show_url has a high cardinality: 55 distinct values High cardinality
_embedded_show_name has a high cardinality: 55 distinct values High cardinality
_embedded_show_officialSite has a high cardinality: 51 distinct values High cardinality
_links_self_href has a high cardinality: 82 distinct values High cardinality
season is highly correlated with number and 1 other fieldsHigh correlation
number is highly correlated with seasonHigh correlation
runtime is highly correlated with _embedded_show_runtime and 1 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded_show_updated is highly correlated with seasonHigh correlation
season is highly correlated with numberHigh correlation
number is highly correlated with seasonHigh correlation
runtime is highly correlated with _embedded_show_runtime and 1 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with runtime and 1 other fieldsHigh correlation
runtime is highly correlated with _embedded_show_runtime and 1 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded_show_officialSite is highly correlated with summary and 17 other fieldsHigh correlation
summary is highly correlated with _embedded_show_officialSite and 7 other fieldsHigh correlation
_embedded_show_summary is highly correlated with _embedded_show_officialSite and 16 other fieldsHigh correlation
_embedded_show_type is highly correlated with _embedded_show_officialSite and 8 other fieldsHigh correlation
_embedded_show_status is highly correlated with _embedded_show_officialSite and 9 other fieldsHigh correlation
url is highly correlated with _embedded_show_officialSite and 17 other fieldsHigh correlation
_embedded_show_ended is highly correlated with _embedded_show_officialSite and 10 other fieldsHigh correlation
_embedded_show_name is highly correlated with _embedded_show_officialSite and 17 other fieldsHigh correlation
_embedded_show_premiered is highly correlated with _embedded_show_officialSite and 14 other fieldsHigh correlation
name is highly correlated with _embedded_show_officialSite and 8 other fieldsHigh correlation
airdate is highly correlated with _embedded_show_officialSite and 17 other fieldsHigh correlation
airtime is highly correlated with _embedded_show_officialSite and 11 other fieldsHigh correlation
_links_self_href is highly correlated with _embedded_show_officialSite and 17 other fieldsHigh correlation
_embedded_show_genres is highly correlated with _embedded_show_officialSite and 10 other fieldsHigh correlation
type is highly correlated with _embedded_show_officialSite and 8 other fieldsHigh correlation
_embedded_show_url is highly correlated with _embedded_show_officialSite and 17 other fieldsHigh correlation
_embedded_show_language is highly correlated with _embedded_show_officialSite and 10 other fieldsHigh correlation
_embedded_show_dvdCountry is highly correlated with _embedded_show_officialSite and 17 other fieldsHigh correlation
airstamp is highly correlated with _embedded_show_officialSite and 11 other fieldsHigh correlation
id is highly correlated with url and 12 other fieldsHigh correlation
url is highly correlated with id and 24 other fieldsHigh correlation
name is highly correlated with id and 23 other fieldsHigh correlation
season is highly correlated with url and 15 other fieldsHigh correlation
number is highly correlated with url and 16 other fieldsHigh correlation
type is highly correlated with url and 11 other fieldsHigh correlation
airtime is highly correlated with url and 19 other fieldsHigh correlation
airstamp is highly correlated with id and 24 other fieldsHigh correlation
runtime is highly correlated with url and 21 other fieldsHigh correlation
summary is highly correlated with url and 17 other fieldsHigh correlation
_embedded_show_id is highly correlated with id and 20 other fieldsHigh correlation
_embedded_show_url is highly correlated with id and 24 other fieldsHigh correlation
_embedded_show_name is highly correlated with id and 24 other fieldsHigh correlation
_embedded_show_type is highly correlated with id and 19 other fieldsHigh correlation
_embedded_show_language is highly correlated with id and 22 other fieldsHigh correlation
_embedded_show_genres is highly correlated with url and 19 other fieldsHigh correlation
_embedded_show_status is highly correlated with url and 16 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with url and 21 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with url and 21 other fieldsHigh correlation
_embedded_show_premiered is highly correlated with id and 24 other fieldsHigh correlation
_embedded_show_ended is highly correlated with url and 18 other fieldsHigh correlation
_embedded_show_officialSite is highly correlated with id and 24 other fieldsHigh correlation
_embedded_show_weight is highly correlated with url and 19 other fieldsHigh correlation
_embedded_show_summary is highly correlated with id and 23 other fieldsHigh correlation
_embedded_show_updated is highly correlated with id and 14 other fieldsHigh correlation
_links_self_href is highly correlated with id and 24 other fieldsHigh correlation
number has 4 (4.9%) missing values Missing
runtime has 3 (3.7%) missing values Missing
_embedded_show_runtime has 6 (7.3%) missing values Missing
_embedded_show_averageRuntime has 1 (1.2%) missing values Missing
url is uniformly distributed Uniform
name is uniformly distributed Uniform
_links_self_href is uniformly distributed Uniform
id has unique values Unique
url has unique values Unique
_links_self_href has unique values Unique

Reproduction

Analysis started2022-05-10 02:17:59.771846
Analysis finished2022-05-10 02:18:30.007768
Duration30.24 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

id
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct82
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2020892.695
Minimum1967930
Maximum2318109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size784.0 B
2022-05-09T21:18:30.078583image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1967930
5-th percentile1972798.5
Q11982851.75
median1991086
Q32005748.75
95-th percentile2197581.5
Maximum2318109
Range350179
Interquartile range (IQR)22897

Descriptive statistics

Standard deviation74587.70837
Coefficient of variation (CV)0.03690829728
Kurtosis4.377662335
Mean2020892.695
Median Absolute Deviation (MAD)10098.5
Skewness2.253098726
Sum165713201
Variance5563326240
MonotonicityNot monotonic
2022-05-09T21:18:30.185784image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19778991
 
1.2%
19985421
 
1.2%
19938171
 
1.2%
19938161
 
1.2%
19938151
 
1.2%
19938141
 
1.2%
19938131
 
1.2%
19938121
 
1.2%
19938111
 
1.2%
19938101
 
1.2%
Other values (72)72
87.8%
ValueCountFrequency (%)
19679301
1.2%
19690631
1.2%
19707681
1.2%
19720591
1.2%
19727131
1.2%
19744231
1.2%
19744241
1.2%
19760421
1.2%
19760431
1.2%
19773291
1.2%
ValueCountFrequency (%)
23181091
1.2%
22432141
1.2%
22396101
1.2%
22111371
1.2%
21975971
1.2%
21972871
1.2%
21761381
1.2%
21641951
1.2%
21525861
1.2%
21403881
1.2%

url
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct82
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size784.0 B
https://www.tvmaze.com/episodes/1977899/obycnaa-zensina-2x03-seria-12
 
1
https://www.tvmaze.com/episodes/1998542/love-script-1x03-episode-3
 
1
https://www.tvmaze.com/episodes/1993817/the-case-solver-1x11-episode-11
 
1
https://www.tvmaze.com/episodes/1993816/the-case-solver-1x10-episode-10
 
1
https://www.tvmaze.com/episodes/1993815/the-case-solver-1x09-episode-9
 
1
Other values (77)
77 

Length

Max length141
Median length95
Mean length79.03658537
Min length58

Characters and Unicode

Total characters6481
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique82 ?
Unique (%)100.0%

Sample

1st rowhttps://www.tvmaze.com/episodes/1977899/obycnaa-zensina-2x03-seria-12
2nd rowhttps://www.tvmaze.com/episodes/2164195/ispoved-1x09-viktoria-bona
3rd rowhttps://www.tvmaze.com/episodes/1982407/volk-1x09-seria-09
4th rowhttps://www.tvmaze.com/episodes/1982408/volk-1x10-seria-10
5th rowhttps://www.tvmaze.com/episodes/1988014/muzskaa-tema-1x03-seria-3

Common Values

ValueCountFrequency (%)
https://www.tvmaze.com/episodes/1977899/obycnaa-zensina-2x03-seria-121
 
1.2%
https://www.tvmaze.com/episodes/1998542/love-script-1x03-episode-31
 
1.2%
https://www.tvmaze.com/episodes/1993817/the-case-solver-1x11-episode-111
 
1.2%
https://www.tvmaze.com/episodes/1993816/the-case-solver-1x10-episode-101
 
1.2%
https://www.tvmaze.com/episodes/1993815/the-case-solver-1x09-episode-91
 
1.2%
https://www.tvmaze.com/episodes/1993814/the-case-solver-1x08-episode-81
 
1.2%
https://www.tvmaze.com/episodes/1993813/the-case-solver-1x07-episode-71
 
1.2%
https://www.tvmaze.com/episodes/1993812/the-case-solver-1x06-episode-61
 
1.2%
https://www.tvmaze.com/episodes/1993811/the-case-solver-1x05-episode-51
 
1.2%
https://www.tvmaze.com/episodes/1993810/the-case-solver-1x04-episode-41
 
1.2%
Other values (72)72
87.8%

Length

2022-05-09T21:18:30.314150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.tvmaze.com/episodes/1977899/obycnaa-zensina-2x03-seria-121
 
1.2%
https://www.tvmaze.com/episodes/1972059/red-vs-blue-18x07-for-power-pt-11
 
1.2%
https://www.tvmaze.com/episodes/1982408/volk-1x10-seria-101
 
1.2%
https://www.tvmaze.com/episodes/1988014/muzskaa-tema-1x03-seria-31
 
1.2%
https://www.tvmaze.com/episodes/2062926/god-of-ten-thousand-realms-1x01-episode-11
 
1.2%
https://www.tvmaze.com/episodes/2062927/god-of-ten-thousand-realms-1x02-episode-21
 
1.2%
https://www.tvmaze.com/episodes/2062928/god-of-ten-thousand-realms-1x03-episode-31
 
1.2%
https://www.tvmaze.com/episodes/2140388/going-seventeen-2020-12-21-going-vs-seventeen-21
 
1.2%
https://www.tvmaze.com/episodes/2005748/legend-of-yun-qian-1x01-episode-11
 
1.2%
https://www.tvmaze.com/episodes/2005749/legend-of-yun-qian-1x02-episode-21
 
1.2%
Other values (72)72
87.8%

Most occurring characters

ValueCountFrequency (%)
e571
 
8.8%
-517
 
8.0%
s447
 
6.9%
/410
 
6.3%
t410
 
6.3%
o353
 
5.4%
w278
 
4.3%
a238
 
3.7%
p234
 
3.6%
i228
 
3.5%
Other values (30)2795
43.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4317
66.6%
Decimal Number991
 
15.3%
Other Punctuation656
 
10.1%
Dash Punctuation517
 
8.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e571
13.2%
s447
 
10.4%
t410
 
9.5%
o353
 
8.2%
w278
 
6.4%
a238
 
5.5%
p234
 
5.4%
i228
 
5.3%
m207
 
4.8%
d191
 
4.4%
Other values (16)1160
26.9%
Decimal Number
ValueCountFrequency (%)
1228
23.0%
2138
13.9%
0128
12.9%
9122
12.3%
385
 
8.6%
879
 
8.0%
765
 
6.6%
458
 
5.9%
647
 
4.7%
541
 
4.1%
Other Punctuation
ValueCountFrequency (%)
/410
62.5%
.164
 
25.0%
:82
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-517
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4317
66.6%
Common2164
33.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e571
13.2%
s447
 
10.4%
t410
 
9.5%
o353
 
8.2%
w278
 
6.4%
a238
 
5.5%
p234
 
5.4%
i228
 
5.3%
m207
 
4.8%
d191
 
4.4%
Other values (16)1160
26.9%
Common
ValueCountFrequency (%)
-517
23.9%
/410
18.9%
1228
10.5%
.164
 
7.6%
2138
 
6.4%
0128
 
5.9%
9122
 
5.6%
385
 
3.9%
:82
 
3.8%
879
 
3.7%
Other values (4)211
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII6481
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e571
 
8.8%
-517
 
8.0%
s447
 
6.9%
/410
 
6.3%
t410
 
6.3%
o353
 
5.4%
w278
 
4.3%
a238
 
3.7%
p234
 
3.6%
i228
 
3.5%
Other values (30)2795
43.1%

name
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct66
Distinct (%)80.5%
Missing0
Missing (%)0.0%
Memory size784.0 B
Episode 7
 
3
Episode 3
 
3
Episode 4
 
3
Episode 6
 
3
Episode 1
 
3
Other values (61)
67 

Length

Max length75
Median length63.5
Mean length16.43902439
Min length7

Characters and Unicode

Total characters1348
Distinct characters99
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique56 ?
Unique (%)68.3%

Sample

1st rowСерия 12
2nd rowВиктория Боня
3rd rowСерия 09
4th rowСерия 10
5th rowСерия 3

Common Values

ValueCountFrequency (%)
Episode 73
 
3.7%
Episode 33
 
3.7%
Episode 43
 
3.7%
Episode 63
 
3.7%
Episode 13
 
3.7%
Episode 23
 
3.7%
Episode 52
 
2.4%
Episode 102
 
2.4%
Episode 92
 
2.4%
Episode 82
 
2.4%
Other values (56)56
68.3%

Length

2022-05-09T21:18:30.422976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
episode39
 
15.2%
16
 
2.3%
35
 
1.9%
5
 
1.9%
25
 
1.9%
серия5
 
1.9%
20204
 
1.6%
214
 
1.6%
the4
 
1.6%
for3
 
1.2%
Other values (146)177
68.9%

Most occurring characters

ValueCountFrequency (%)
175
 
13.0%
e99
 
7.3%
o87
 
6.5%
i76
 
5.6%
s70
 
5.2%
d54
 
4.0%
r52
 
3.9%
E50
 
3.7%
a47
 
3.5%
p44
 
3.3%
Other values (89)594
44.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter816
60.5%
Uppercase Letter206
 
15.3%
Space Separator175
 
13.0%
Decimal Number114
 
8.5%
Other Punctuation26
 
1.9%
Dash Punctuation7
 
0.5%
Final Punctuation1
 
0.1%
Initial Punctuation1
 
0.1%
Close Punctuation1
 
0.1%
Open Punctuation1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e99
12.1%
o87
 
10.7%
i76
 
9.3%
s70
 
8.6%
d54
 
6.6%
r52
 
6.4%
a47
 
5.8%
p44
 
5.4%
t40
 
4.9%
n28
 
3.4%
Other values (37)219
26.8%
Uppercase Letter
ValueCountFrequency (%)
E50
24.3%
S19
 
9.2%
T15
 
7.3%
L12
 
5.8%
B9
 
4.4%
F9
 
4.4%
D9
 
4.4%
N8
 
3.9%
W6
 
2.9%
R6
 
2.9%
Other values (19)63
30.6%
Decimal Number
ValueCountFrequency (%)
126
22.8%
222
19.3%
017
14.9%
315
13.2%
58
 
7.0%
68
 
7.0%
46
 
5.3%
95
 
4.4%
84
 
3.5%
73
 
2.6%
Other Punctuation
ValueCountFrequency (%)
,10
38.5%
#6
23.1%
:3
 
11.5%
'2
 
7.7%
.2
 
7.7%
?2
 
7.7%
!1
 
3.8%
Space Separator
ValueCountFrequency (%)
175
100.0%
Dash Punctuation
ValueCountFrequency (%)
-7
100.0%
Final Punctuation
ValueCountFrequency (%)
»1
100.0%
Initial Punctuation
ValueCountFrequency (%)
«1
100.0%
Close Punctuation
ValueCountFrequency (%)
)1
100.0%
Open Punctuation
ValueCountFrequency (%)
(1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin922
68.4%
Common326
 
24.2%
Cyrillic100
 
7.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e99
 
10.7%
o87
 
9.4%
i76
 
8.2%
s70
 
7.6%
d54
 
5.9%
r52
 
5.6%
E50
 
5.4%
a47
 
5.1%
p44
 
4.8%
t40
 
4.3%
Other values (37)303
32.9%
Cyrillic
ValueCountFrequency (%)
и15
15.0%
я9
 
9.0%
л8
 
8.0%
е7
 
7.0%
р7
 
7.0%
а5
 
5.0%
к5
 
5.0%
С5
 
5.0%
у4
 
4.0%
о4
 
4.0%
Other values (19)31
31.0%
Common
ValueCountFrequency (%)
175
53.7%
126
 
8.0%
222
 
6.7%
017
 
5.2%
315
 
4.6%
,10
 
3.1%
58
 
2.5%
68
 
2.5%
-7
 
2.1%
#6
 
1.8%
Other values (13)32
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1246
92.4%
Cyrillic100
 
7.4%
None2
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
175
 
14.0%
e99
 
7.9%
o87
 
7.0%
i76
 
6.1%
s70
 
5.6%
d54
 
4.3%
r52
 
4.2%
E50
 
4.0%
a47
 
3.8%
p44
 
3.5%
Other values (58)492
39.5%
Cyrillic
ValueCountFrequency (%)
и15
15.0%
я9
 
9.0%
л8
 
8.0%
е7
 
7.0%
р7
 
7.0%
а5
 
5.0%
к5
 
5.0%
С5
 
5.0%
у4
 
4.0%
о4
 
4.0%
Other values (19)31
31.0%
None
ValueCountFrequency (%)
»1
50.0%
«1
50.0%

season
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean273.8902439
Minimum1
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size784.0 B
2022-05-09T21:18:30.547002image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile2020
Maximum2020
Range2019
Interquartile range (IQR)3

Descriptive statistics

Standard deviation691.5070857
Coefficient of variation (CV)2.52475983
Kurtosis2.851953774
Mean273.8902439
Median Absolute Deviation (MAD)0
Skewness2.186852915
Sum22459
Variance478182.0495
MonotonicityNot monotonic
2022-05-09T21:18:30.640070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
151
62.2%
202010
 
12.2%
45
 
6.1%
24
 
4.9%
33
 
3.7%
182
 
2.4%
91
 
1.2%
301
 
1.2%
71
 
1.2%
121
 
1.2%
Other values (3)3
 
3.7%
ValueCountFrequency (%)
151
62.2%
24
 
4.9%
33
 
3.7%
45
 
6.1%
71
 
1.2%
91
 
1.2%
121
 
1.2%
182
 
2.4%
271
 
1.2%
301
 
1.2%
ValueCountFrequency (%)
202010
12.2%
20191
 
1.2%
311
 
1.2%
301
 
1.2%
271
 
1.2%
182
 
2.4%
121
 
1.2%
91
 
1.2%
71
 
1.2%
45
6.1%

number
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct36
Distinct (%)46.2%
Missing4
Missing (%)4.9%
Infinite0
Infinite (%)0.0%
Mean33.33333333
Minimum1
Maximum348
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size784.0 B
2022-05-09T21:18:30.750741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median10
Q331.75
95-th percentile110.05
Maximum348
Range347
Interquartile range (IQR)26.75

Descriptive statistics

Standard deviation66.70124644
Coefficient of variation (CV)2.001037393
Kurtosis13.53444224
Mean33.33333333
Median Absolute Deviation (MAD)7
Skewness3.674838254
Sum2600
Variance4449.056277
MonotonicityNot monotonic
2022-05-09T21:18:30.845362image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
37
 
8.5%
15
 
6.1%
95
 
6.1%
74
 
4.9%
104
 
4.9%
24
 
4.9%
514
 
4.9%
114
 
4.9%
43
 
3.7%
63
 
3.7%
Other values (26)35
42.7%
(Missing)4
 
4.9%
ValueCountFrequency (%)
15
6.1%
24
4.9%
37
8.5%
43
3.7%
53
3.7%
63
3.7%
74
4.9%
82
 
2.4%
95
6.1%
104
4.9%
ValueCountFrequency (%)
3481
1.2%
3121
1.2%
3111
1.2%
2351
1.2%
881
1.2%
851
1.2%
701
1.2%
651
1.2%
591
1.2%
531
1.2%

type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size784.0 B
regular
78 
significant_special
 
3
insignificant_special
 
1

Length

Max length21
Median length7
Mean length7.609756098
Min length7

Characters and Unicode

Total characters624
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.2%

Sample

1st rowregular
2nd rowregular
3rd rowregular
4th rowregular
5th rowregular

Common Values

ValueCountFrequency (%)
regular78
95.1%
significant_special3
 
3.7%
insignificant_special1
 
1.2%

Length

2022-05-09T21:18:30.955283image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:18:31.049123image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
regular78
95.1%
significant_special3
 
3.7%
insignificant_special1
 
1.2%

Most occurring characters

ValueCountFrequency (%)
r156
25.0%
a86
13.8%
e82
13.1%
g82
13.1%
l82
13.1%
u78
12.5%
i17
 
2.7%
n9
 
1.4%
s8
 
1.3%
c8
 
1.3%
Other values (4)16
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter620
99.4%
Connector Punctuation4
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r156
25.2%
a86
13.9%
e82
13.2%
g82
13.2%
l82
13.2%
u78
12.6%
i17
 
2.7%
n9
 
1.5%
s8
 
1.3%
c8
 
1.3%
Other values (3)12
 
1.9%
Connector Punctuation
ValueCountFrequency (%)
_4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin620
99.4%
Common4
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
r156
25.2%
a86
13.9%
e82
13.2%
g82
13.2%
l82
13.2%
u78
12.6%
i17
 
2.7%
n9
 
1.5%
s8
 
1.3%
c8
 
1.3%
Other values (3)12
 
1.9%
Common
ValueCountFrequency (%)
_4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r156
25.0%
a86
13.8%
e82
13.1%
g82
13.1%
l82
13.1%
u78
12.5%
i17
 
2.7%
n9
 
1.4%
s8
 
1.3%
c8
 
1.3%
Other values (4)16
 
2.6%

airdate
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size784.0 B
2020-12-21
82 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters820
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-12-21
2nd row2020-12-21
3rd row2020-12-21
4th row2020-12-21
5th row2020-12-21

Common Values

ValueCountFrequency (%)
2020-12-2182
100.0%

Length

2022-05-09T21:18:31.143245image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:18:31.221777image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-12-2182
100.0%

Most occurring characters

ValueCountFrequency (%)
2328
40.0%
0164
20.0%
-164
20.0%
1164
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number656
80.0%
Dash Punctuation164
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2328
50.0%
0164
25.0%
1164
25.0%
Dash Punctuation
ValueCountFrequency (%)
-164
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common820
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2328
40.0%
0164
20.0%
-164
20.0%
1164
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII820
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2328
40.0%
0164
20.0%
-164
20.0%
1164
20.0%

airtime
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct11
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Memory size784.0 B
nan
52 
20:00
16 
10:00
 
4
12:00
 
2
21:00
 
2
Other values (6)

Length

Max length5
Median length3
Mean length3.731707317
Min length3

Characters and Unicode

Total characters306
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)7.3%

Sample

1st row10:00
2nd row12:00
3rd rownan
4th rownan
5th row12:00

Common Values

ValueCountFrequency (%)
nan52
63.4%
20:0016
 
19.5%
10:004
 
4.9%
12:002
 
2.4%
21:002
 
2.4%
06:001
 
1.2%
18:301
 
1.2%
20:451
 
1.2%
00:001
 
1.2%
19:001
 
1.2%

Length

2022-05-09T21:18:31.315943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nan52
63.4%
20:0016
 
19.5%
10:004
 
4.9%
12:002
 
2.4%
21:002
 
2.4%
06:001
 
1.2%
18:301
 
1.2%
20:451
 
1.2%
00:001
 
1.2%
19:001
 
1.2%

Most occurring characters

ValueCountFrequency (%)
n104
34.0%
081
26.5%
a52
17.0%
:30
 
9.8%
222
 
7.2%
110
 
3.3%
32
 
0.7%
61
 
0.3%
81
 
0.3%
41
 
0.3%
Other values (2)2
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter156
51.0%
Decimal Number120
39.2%
Other Punctuation30
 
9.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
081
67.5%
222
 
18.3%
110
 
8.3%
32
 
1.7%
61
 
0.8%
81
 
0.8%
41
 
0.8%
51
 
0.8%
91
 
0.8%
Lowercase Letter
ValueCountFrequency (%)
n104
66.7%
a52
33.3%
Other Punctuation
ValueCountFrequency (%)
:30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin156
51.0%
Common150
49.0%

Most frequent character per script

Common
ValueCountFrequency (%)
081
54.0%
:30
 
20.0%
222
 
14.7%
110
 
6.7%
32
 
1.3%
61
 
0.7%
81
 
0.7%
41
 
0.7%
51
 
0.7%
91
 
0.7%
Latin
ValueCountFrequency (%)
n104
66.7%
a52
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII306
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n104
34.0%
081
26.5%
a52
17.0%
:30
 
9.8%
222
 
7.2%
110
 
3.3%
32
 
0.7%
61
 
0.3%
81
 
0.3%
41
 
0.3%
Other values (2)2
 
0.7%

airstamp
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct19
Distinct (%)23.2%
Missing0
Missing (%)0.0%
Memory size784.0 B
2020-12-21T12:00:00+00:00
49 
2020-12-21T17:00:00+00:00
2020-12-21T04:00:00+00:00
2020-12-21T00:00:00+00:00
 
4
2020-12-21T02:00:00+00:00
 
3
Other values (14)
15 

Length

Max length25
Median length25
Mean length25
Min length25

Characters and Unicode

Total characters2050
Distinct characters14
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)15.9%

Sample

1st row2020-12-20T22:00:00+00:00
2nd row2020-12-21T00:00:00+00:00
3rd row2020-12-21T00:00:00+00:00
4th row2020-12-21T00:00:00+00:00
5th row2020-12-21T00:00:00+00:00

Common Values

ValueCountFrequency (%)
2020-12-21T12:00:00+00:0049
59.8%
2020-12-21T17:00:00+00:006
 
7.3%
2020-12-21T04:00:00+00:005
 
6.1%
2020-12-21T00:00:00+00:004
 
4.9%
2020-12-21T02:00:00+00:003
 
3.7%
2020-12-21T11:00:00+00:002
 
2.4%
2020-12-21T16:00:00+00:001
 
1.2%
2020-12-22T01:00:00+00:001
 
1.2%
2020-12-21T23:00:00+00:001
 
1.2%
2020-12-21T21:00:00+00:001
 
1.2%
Other values (9)9
 
11.0%

Length

2022-05-09T21:18:31.526724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-12-21t12:00:00+00:0049
59.8%
2020-12-21t17:00:00+00:006
 
7.3%
2020-12-21t04:00:00+00:005
 
6.1%
2020-12-21t00:00:00+00:004
 
4.9%
2020-12-21t02:00:00+00:003
 
3.7%
2020-12-21t11:00:00+00:002
 
2.4%
2020-12-21t15:00:00+00:001
 
1.2%
2020-12-21t03:00:00+00:001
 
1.2%
2020-12-21t05:00:00+00:001
 
1.2%
2020-12-21t08:00:00+00:001
 
1.2%
Other values (9)9
 
11.0%

Most occurring characters

ValueCountFrequency (%)
0840
41.0%
2388
18.9%
:246
 
12.0%
1226
 
11.0%
-164
 
8.0%
T82
 
4.0%
+82
 
4.0%
76
 
0.3%
46
 
0.3%
33
 
0.1%
Other values (4)7
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1476
72.0%
Other Punctuation246
 
12.0%
Dash Punctuation164
 
8.0%
Uppercase Letter82
 
4.0%
Math Symbol82
 
4.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0840
56.9%
2388
26.3%
1226
 
15.3%
76
 
0.4%
46
 
0.4%
33
 
0.2%
53
 
0.2%
92
 
0.1%
61
 
0.1%
81
 
0.1%
Other Punctuation
ValueCountFrequency (%)
:246
100.0%
Dash Punctuation
ValueCountFrequency (%)
-164
100.0%
Uppercase Letter
ValueCountFrequency (%)
T82
100.0%
Math Symbol
ValueCountFrequency (%)
+82
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1968
96.0%
Latin82
 
4.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0840
42.7%
2388
19.7%
:246
 
12.5%
1226
 
11.5%
-164
 
8.3%
+82
 
4.2%
76
 
0.3%
46
 
0.3%
33
 
0.2%
53
 
0.2%
Other values (3)4
 
0.2%
Latin
ValueCountFrequency (%)
T82
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2050
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0840
41.0%
2388
18.9%
:246
 
12.0%
1226
 
11.0%
-164
 
8.0%
T82
 
4.0%
+82
 
4.0%
76
 
0.3%
46
 
0.3%
33
 
0.1%
Other values (4)7
 
0.3%

runtime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct29
Distinct (%)36.7%
Missing3
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean37.40506329
Minimum2
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size784.0 B
2022-05-09T21:18:31.605275image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4.9
Q115
median29
Q345
95-th percentile120
Maximum180
Range178
Interquartile range (IQR)30

Descriptive statistics

Standard deviation32.9126735
Coefficient of variation (CV)0.8798988853
Kurtosis5.131646369
Mean37.40506329
Median Absolute Deviation (MAD)16
Skewness2.063084831
Sum2955
Variance1083.244077
MonotonicityNot monotonic
2022-05-09T21:18:31.724391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2712
14.6%
459
 
11.0%
308
 
9.8%
75
 
6.1%
605
 
6.1%
1204
 
4.9%
153
 
3.7%
113
 
3.7%
483
 
3.7%
22
 
2.4%
Other values (19)25
30.5%
(Missing)3
 
3.7%
ValueCountFrequency (%)
22
 
2.4%
42
 
2.4%
52
 
2.4%
75
6.1%
102
 
2.4%
113
3.7%
122
 
2.4%
153
3.7%
181
 
1.2%
202
 
2.4%
ValueCountFrequency (%)
1801
 
1.2%
1301
 
1.2%
1204
4.9%
901
 
1.2%
605
6.1%
551
 
1.2%
512
 
2.4%
501
 
1.2%
483
 
3.7%
459
11.0%

summary
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Memory size784.0 B
nan
71 
<p>Zero is moments away from achieving the ultimate power! Can Shatter Squad stop him in time? The clocks ticking... </p>
 
1
<p>Rachael Ray makes her own Chinese-style pork sausage for her oversized vegetable and protein omelets.</p>
 
1
<p>After Peter has brought his fiancée Grace to safety in New Zealand, he travels to Dublin, where Zora has ensured in his absence that Peter not only loses his share of the family empire, but also has to go to prison for infidelity. In return for a small favor, Vincent helps the Europol agent Neumann to put Tariq Bassari behind bars.</p>
 
1
<p>Zora believes her goals are within reach, but she did the math without Vincent. His force is now also supported by the former Europol agent Neumann, who left the agency disaffected. While Peter, Vincent and the others do everything they can to prevent the rocket launch, the Russian investors are sitting on Zora's neck and will not tolerate any delay.</p>
 
1
Other values (7)
 
7

Length

Max length359
Median length3
Mean length27.17073171
Min length3

Characters and Unicode

Total characters2228
Distinct characters58
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)13.4%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th rownan

Common Values

ValueCountFrequency (%)
nan71
86.6%
<p>Zero is moments away from achieving the ultimate power! Can Shatter Squad stop him in time? The clocks ticking... </p>1
 
1.2%
<p>Rachael Ray makes her own Chinese-style pork sausage for her oversized vegetable and protein omelets.</p>1
 
1.2%
<p>After Peter has brought his fiancée Grace to safety in New Zealand, he travels to Dublin, where Zora has ensured in his absence that Peter not only loses his share of the family empire, but also has to go to prison for infidelity. In return for a small favor, Vincent helps the Europol agent Neumann to put Tariq Bassari behind bars.</p>1
 
1.2%
<p>Zora believes her goals are within reach, but she did the math without Vincent. His force is now also supported by the former Europol agent Neumann, who left the agency disaffected. While Peter, Vincent and the others do everything they can to prevent the rocket launch, the Russian investors are sitting on Zora's neck and will not tolerate any delay.</p>1
 
1.2%
<p>Tan and Bun receive a strange visitor, Pat, on the night of his birthday. After a few drinks, Pat reveals his own theories behind Janejira's death. </p>1
 
1.2%
<p>The three remaining contestants race to the second elimination point where a dramatic change occurs in the race.</p>1
 
1.2%
<p>Heart struggles with a recent breakup.  Jamie finds a connection to a podcaster, Win.  </p>1
 
1.2%
<p>James and Dale meet for the first time under less than favorable circumstances. But things could be looking up.</p>1
 
1.2%
<p>Join Gus Sorola, Gavin Free, Drew Saplin, and Barbara Dunkelman as they discuss Drew's hate for the moon, astrology, climbing very tall mountains and bouncing, and more on this week's RT Podcast!</p>1
 
1.2%
Other values (2)2
 
2.4%

Length

2022-05-09T21:18:31.834464image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nan71
 
17.8%
the17
 
4.2%
and13
 
3.2%
to8
 
2.0%
a8
 
2.0%
in7
 
1.8%
his6
 
1.5%
of6
 
1.5%
for5
 
1.2%
has4
 
1.0%
Other values (222)255
63.7%

Most occurring characters

ValueCountFrequency (%)
316
14.2%
n266
11.9%
a206
 
9.2%
e189
 
8.5%
t130
 
5.8%
o110
 
4.9%
i106
 
4.8%
r101
 
4.5%
s97
 
4.4%
h78
 
3.5%
Other values (48)629
28.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1719
77.2%
Space Separator320
 
14.4%
Uppercase Letter82
 
3.7%
Other Punctuation61
 
2.7%
Math Symbol44
 
2.0%
Dash Punctuation2
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n266
15.5%
a206
12.0%
e189
11.0%
t130
 
7.6%
o110
 
6.4%
i106
 
6.2%
r101
 
5.9%
s97
 
5.6%
h78
 
4.5%
l58
 
3.4%
Other values (16)378
22.0%
Uppercase Letter
ValueCountFrequency (%)
S11
13.4%
D6
 
7.3%
B6
 
7.3%
J6
 
7.3%
P6
 
7.3%
T6
 
7.3%
Z5
 
6.1%
W4
 
4.9%
R4
 
4.9%
C4
 
4.9%
Other values (11)24
29.3%
Other Punctuation
ValueCountFrequency (%)
,22
36.1%
.19
31.1%
/11
18.0%
'6
 
9.8%
!2
 
3.3%
?1
 
1.6%
Space Separator
ValueCountFrequency (%)
316
98.8%
 4
 
1.2%
Math Symbol
ValueCountFrequency (%)
>22
50.0%
<22
50.0%
Dash Punctuation
ValueCountFrequency (%)
-2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1801
80.8%
Common427
 
19.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
n266
14.8%
a206
11.4%
e189
10.5%
t130
 
7.2%
o110
 
6.1%
i106
 
5.9%
r101
 
5.6%
s97
 
5.4%
h78
 
4.3%
l58
 
3.2%
Other values (37)460
25.5%
Common
ValueCountFrequency (%)
316
74.0%
>22
 
5.2%
<22
 
5.2%
,22
 
5.2%
.19
 
4.4%
/11
 
2.6%
'6
 
1.4%
 4
 
0.9%
!2
 
0.5%
-2
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII2223
99.8%
None5
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
316
14.2%
n266
12.0%
a206
 
9.3%
e189
 
8.5%
t130
 
5.8%
o110
 
4.9%
i106
 
4.8%
r101
 
4.5%
s97
 
4.4%
h78
 
3.5%
Other values (46)624
28.1%
None
ValueCountFrequency (%)
 4
80.0%
é1
 
20.0%

_embedded_show_id
Real number (ℝ≥0)

HIGH CORRELATION

Distinct55
Distinct (%)67.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44156.58537
Minimum802
Maximum61755
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size784.0 B
2022-05-09T21:18:31.957214image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum802
5-th percentile6147
Q142412
median52222.5
Q352655
95-th percentile58299.1
Maximum61755
Range60953
Interquartile range (IQR)10243

Descriptive statistics

Standard deviation16095.5427
Coefficient of variation (CV)0.3645105835
Kurtosis1.133232181
Mean44156.58537
Median Absolute Deviation (MAD)2463.5
Skewness-1.573678419
Sum3620840
Variance259066494.7
MonotonicityNot monotonic
2022-05-09T21:18:32.068141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5265512
 
14.6%
524795
 
6.1%
545413
 
3.7%
61472
 
2.4%
525242
 
2.4%
521592
 
2.4%
521042
 
2.4%
527812
 
2.4%
424122
 
2.4%
152502
 
2.4%
Other values (45)48
58.5%
ValueCountFrequency (%)
8021
1.2%
25041
1.2%
60901
1.2%
61461
1.2%
61472
2.4%
72411
1.2%
98151
1.2%
152502
2.4%
175841
1.2%
189711
1.2%
ValueCountFrequency (%)
617551
 
1.2%
595551
 
1.2%
588211
 
1.2%
584261
 
1.2%
583671
 
1.2%
570091
 
1.2%
566551
 
1.2%
547622
2.4%
546101
 
1.2%
545413
3.7%

_embedded_show_url
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct55
Distinct (%)67.1%
Missing0
Missing (%)0.0%
Memory size784.0 B
https://www.tvmaze.com/shows/52655/the-case-solver
12 
https://www.tvmaze.com/shows/52479/beauty-and-the-boss
 
5
https://www.tvmaze.com/shows/54541/god-of-ten-thousand-realms
 
3
https://www.tvmaze.com/shows/6147/rooster-teeth-animated-adventures
 
2
https://www.tvmaze.com/shows/52524/forever-love
 
2
Other values (50)
58 

Length

Max length67
Median length61
Mean length50.80487805
Min length39

Characters and Unicode

Total characters4166
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)51.2%

Sample

1st rowhttps://www.tvmaze.com/shows/39115/obycnaa-zensina
2nd rowhttps://www.tvmaze.com/shows/48683/ispoved
3rd rowhttps://www.tvmaze.com/shows/52181/volk
4th rowhttps://www.tvmaze.com/shows/52181/volk
5th rowhttps://www.tvmaze.com/shows/52520/muzskaa-tema

Common Values

ValueCountFrequency (%)
https://www.tvmaze.com/shows/52655/the-case-solver12
 
14.6%
https://www.tvmaze.com/shows/52479/beauty-and-the-boss5
 
6.1%
https://www.tvmaze.com/shows/54541/god-of-ten-thousand-realms3
 
3.7%
https://www.tvmaze.com/shows/6147/rooster-teeth-animated-adventures2
 
2.4%
https://www.tvmaze.com/shows/52524/forever-love2
 
2.4%
https://www.tvmaze.com/shows/52159/to-love2
 
2.4%
https://www.tvmaze.com/shows/52104/twisted-fate-of-love2
 
2.4%
https://www.tvmaze.com/shows/52781/love-script2
 
2.4%
https://www.tvmaze.com/shows/42412/professionals2
 
2.4%
https://www.tvmaze.com/shows/15250/the-young-turks2
 
2.4%
Other values (45)48
58.5%

Length

2022-05-09T21:18:32.177973image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.tvmaze.com/shows/52655/the-case-solver12
 
14.6%
https://www.tvmaze.com/shows/52479/beauty-and-the-boss5
 
6.1%
https://www.tvmaze.com/shows/54541/god-of-ten-thousand-realms3
 
3.7%
https://www.tvmaze.com/shows/42412/professionals2
 
2.4%
https://www.tvmaze.com/shows/54762/youths-in-the-breeze2
 
2.4%
https://www.tvmaze.com/shows/52181/volk2
 
2.4%
https://www.tvmaze.com/shows/15250/the-young-turks2
 
2.4%
https://www.tvmaze.com/shows/52898/legend-of-yun-qian2
 
2.4%
https://www.tvmaze.com/shows/52781/love-script2
 
2.4%
https://www.tvmaze.com/shows/52104/twisted-fate-of-love2
 
2.4%
Other values (45)48
58.5%

Most occurring characters

ValueCountFrequency (%)
/410
 
9.8%
t352
 
8.4%
w347
 
8.3%
s342
 
8.2%
o257
 
6.2%
e247
 
5.9%
h214
 
5.1%
m187
 
4.5%
a170
 
4.1%
.164
 
3.9%
Other values (30)1476
35.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2950
70.8%
Other Punctuation656
 
15.7%
Decimal Number403
 
9.7%
Dash Punctuation157
 
3.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t352
11.9%
w347
11.8%
s342
11.6%
o257
8.7%
e247
8.4%
h214
 
7.3%
m187
 
6.3%
a170
 
5.8%
v115
 
3.9%
c111
 
3.8%
Other values (16)608
20.6%
Decimal Number
ValueCountFrequency (%)
5104
25.8%
259
14.6%
451
12.7%
142
10.4%
639
 
9.7%
925
 
6.2%
725
 
6.2%
825
 
6.2%
021
 
5.2%
312
 
3.0%
Other Punctuation
ValueCountFrequency (%)
/410
62.5%
.164
 
25.0%
:82
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-157
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2950
70.8%
Common1216
29.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
t352
11.9%
w347
11.8%
s342
11.6%
o257
8.7%
e247
8.4%
h214
 
7.3%
m187
 
6.3%
a170
 
5.8%
v115
 
3.9%
c111
 
3.8%
Other values (16)608
20.6%
Common
ValueCountFrequency (%)
/410
33.7%
.164
 
13.5%
-157
 
12.9%
5104
 
8.6%
:82
 
6.7%
259
 
4.9%
451
 
4.2%
142
 
3.5%
639
 
3.2%
925
 
2.1%
Other values (4)83
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII4166
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/410
 
9.8%
t352
 
8.4%
w347
 
8.3%
s342
 
8.2%
o257
 
6.2%
e247
 
5.9%
h214
 
5.1%
m187
 
4.5%
a170
 
4.1%
.164
 
3.9%
Other values (30)1476
35.4%

_embedded_show_name
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct55
Distinct (%)67.1%
Missing0
Missing (%)0.0%
Memory size784.0 B
The Case Solver
12 
Beauty and the Boss
 
5
God of Ten Thousand Realms
 
3
Rooster Teeth Animated Adventures
 
2
Forever Love
 
2
Other values (50)
58 

Length

Max length33
Median length26
Mean length16.01219512
Min length4

Characters and Unicode

Total characters1313
Distinct characters86
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)51.2%

Sample

1st rowОбычная женщина
2nd rowИсповедь
3rd rowВолк
4th rowВолк
5th rowМужская тема

Common Values

ValueCountFrequency (%)
The Case Solver12
 
14.6%
Beauty and the Boss5
 
6.1%
God of Ten Thousand Realms3
 
3.7%
Rooster Teeth Animated Adventures2
 
2.4%
Forever Love2
 
2.4%
To Love2
 
2.4%
Twisted Fate of Love2
 
2.4%
Love Script2
 
2.4%
Professionals2
 
2.4%
The Young Turks2
 
2.4%
Other values (45)48
58.5%

Length

2022-05-09T21:18:32.272220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the25
 
10.4%
solver12
 
5.0%
case12
 
5.0%
of11
 
4.6%
love8
 
3.3%
and6
 
2.5%
beauty5
 
2.1%
boss5
 
2.1%
ten4
 
1.7%
rooster3
 
1.2%
Other values (118)149
62.1%

Most occurring characters

ValueCountFrequency (%)
158
 
12.0%
e153
 
11.7%
o80
 
6.1%
a69
 
5.3%
s65
 
5.0%
t61
 
4.6%
n58
 
4.4%
r53
 
4.0%
h48
 
3.7%
i43
 
3.3%
Other values (76)525
40.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter926
70.5%
Uppercase Letter221
 
16.8%
Space Separator158
 
12.0%
Other Punctuation6
 
0.5%
Decimal Number2
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e153
16.5%
o80
 
8.6%
a69
 
7.5%
s65
 
7.0%
t61
 
6.6%
n58
 
6.3%
r53
 
5.7%
h48
 
5.2%
i43
 
4.6%
d41
 
4.4%
Other values (40)255
27.5%
Uppercase Letter
ValueCountFrequency (%)
T42
19.0%
S26
11.8%
B17
 
7.7%
C16
 
7.2%
R14
 
6.3%
L13
 
5.9%
W11
 
5.0%
M11
 
5.0%
A11
 
5.0%
G8
 
3.6%
Other values (19)52
23.5%
Other Punctuation
ValueCountFrequency (%)
.2
33.3%
'2
33.3%
,1
16.7%
/1
16.7%
Decimal Number
ValueCountFrequency (%)
01
50.0%
31
50.0%
Space Separator
ValueCountFrequency (%)
158
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1069
81.4%
Common166
 
12.6%
Cyrillic78
 
5.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e153
 
14.3%
o80
 
7.5%
a69
 
6.5%
s65
 
6.1%
t61
 
5.7%
n58
 
5.4%
r53
 
5.0%
h48
 
4.5%
i43
 
4.0%
T42
 
3.9%
Other values (40)397
37.1%
Cyrillic
ValueCountFrequency (%)
е8
 
10.3%
н7
 
9.0%
о6
 
7.7%
а6
 
7.7%
к5
 
6.4%
В4
 
5.1%
р3
 
3.8%
и3
 
3.8%
т3
 
3.8%
п3
 
3.8%
Other values (19)30
38.5%
Common
ValueCountFrequency (%)
158
95.2%
.2
 
1.2%
'2
 
1.2%
,1
 
0.6%
/1
 
0.6%
01
 
0.6%
31
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1233
93.9%
Cyrillic78
 
5.9%
None2
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
158
 
12.8%
e153
 
12.4%
o80
 
6.5%
a69
 
5.6%
s65
 
5.3%
t61
 
4.9%
n58
 
4.7%
r53
 
4.3%
h48
 
3.9%
i43
 
3.5%
Other values (45)445
36.1%
Cyrillic
ValueCountFrequency (%)
е8
 
10.3%
н7
 
9.0%
о6
 
7.7%
а6
 
7.7%
к5
 
6.4%
В4
 
5.1%
р3
 
3.8%
и3
 
3.8%
т3
 
3.8%
п3
 
3.8%
Other values (19)30
38.5%
None
ValueCountFrequency (%)
ø1
50.0%
Ç1
50.0%

_embedded_show_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Memory size784.0 B
Scripted
50 
Talk Show
Animation
Reality
 
5
Documentary
 
3
Other values (4)

Length

Max length11
Median length8
Mean length8.012195122
Min length4

Characters and Unicode

Total characters657
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.2%

Sample

1st rowScripted
2nd rowDocumentary
3rd rowScripted
4th rowScripted
5th rowTalk Show

Common Values

ValueCountFrequency (%)
Scripted50
61.0%
Talk Show8
 
9.8%
Animation7
 
8.5%
Reality5
 
6.1%
Documentary3
 
3.7%
Variety3
 
3.7%
News3
 
3.7%
Sports2
 
2.4%
Game Show1
 
1.2%

Length

2022-05-09T21:18:32.383860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:18:32.489518image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
scripted50
54.9%
show9
 
9.9%
talk8
 
8.8%
animation7
 
7.7%
reality5
 
5.5%
documentary3
 
3.3%
variety3
 
3.3%
news3
 
3.3%
sports2
 
2.2%
game1
 
1.1%

Most occurring characters

ValueCountFrequency (%)
i72
11.0%
t70
10.7%
e65
9.9%
S61
9.3%
r58
8.8%
c53
8.1%
p52
7.9%
d50
 
7.6%
a27
 
4.1%
o21
 
3.2%
Other values (17)128
19.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter557
84.8%
Uppercase Letter91
 
13.9%
Space Separator9
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i72
12.9%
t70
12.6%
e65
11.7%
r58
10.4%
c53
9.5%
p52
9.3%
d50
9.0%
a27
 
4.8%
o21
 
3.8%
n17
 
3.1%
Other values (8)72
12.9%
Uppercase Letter
ValueCountFrequency (%)
S61
67.0%
T8
 
8.8%
A7
 
7.7%
R5
 
5.5%
D3
 
3.3%
V3
 
3.3%
N3
 
3.3%
G1
 
1.1%
Space Separator
ValueCountFrequency (%)
9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin648
98.6%
Common9
 
1.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
i72
11.1%
t70
10.8%
e65
10.0%
S61
9.4%
r58
9.0%
c53
8.2%
p52
8.0%
d50
7.7%
a27
 
4.2%
o21
 
3.2%
Other values (16)119
18.4%
Common
ValueCountFrequency (%)
9
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII657
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i72
11.0%
t70
10.7%
e65
9.9%
S61
9.3%
r58
8.8%
c53
8.1%
p52
7.9%
d50
 
7.6%
a27
 
4.1%
o21
 
3.2%
Other values (17)128
19.5%

_embedded_show_language
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct16
Distinct (%)19.5%
Missing0
Missing (%)0.0%
Memory size784.0 B
Chinese
33 
English
22 
Russian
Norwegian
Korean
 
2
Other values (11)
13 

Length

Max length10
Median length7
Mean length6.951219512
Min length3

Characters and Unicode

Total characters570
Distinct characters30
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)11.0%

Sample

1st rowRussian
2nd rowRussian
3rd rowRussian
4th rowRussian
5th rowRussian

Common Values

ValueCountFrequency (%)
Chinese33
40.2%
English22
26.8%
Russian8
 
9.8%
Norwegian4
 
4.9%
Korean2
 
2.4%
Thai2
 
2.4%
Tagalog2
 
2.4%
Japanese1
 
1.2%
Polish1
 
1.2%
Spanish1
 
1.2%
Other values (6)6
 
7.3%

Length

2022-05-09T21:18:32.616024image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
chinese33
40.2%
english22
26.8%
russian8
 
9.8%
norwegian4
 
4.9%
korean2
 
2.4%
thai2
 
2.4%
tagalog2
 
2.4%
japanese1
 
1.2%
polish1
 
1.2%
spanish1
 
1.2%
Other values (6)6
 
7.3%

Most occurring characters

ValueCountFrequency (%)
i76
13.3%
n76
13.3%
s75
13.2%
e74
13.0%
h62
10.9%
C33
5.8%
g30
 
5.3%
a29
 
5.1%
l25
 
4.4%
E22
 
3.9%
Other values (20)68
11.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter489
85.8%
Uppercase Letter81
 
14.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i76
15.5%
n76
15.5%
s75
15.3%
e74
15.1%
h62
12.7%
g30
 
6.1%
a29
 
5.9%
l25
 
5.1%
u11
 
2.2%
o9
 
1.8%
Other values (8)22
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
C33
40.7%
E22
27.2%
R8
 
9.9%
T5
 
6.2%
N4
 
4.9%
K2
 
2.5%
L2
 
2.5%
J1
 
1.2%
P1
 
1.2%
S1
 
1.2%
Other values (2)2
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Latin570
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i76
13.3%
n76
13.3%
s75
13.2%
e74
13.0%
h62
10.9%
C33
5.8%
g30
 
5.3%
a29
 
5.1%
l25
 
4.4%
E22
 
3.9%
Other values (20)68
11.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII570
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i76
13.3%
n76
13.3%
s75
13.2%
e74
13.0%
h62
10.9%
C33
5.8%
g30
 
5.3%
a29
 
5.1%
l25
 
4.4%
E22
 
3.9%
Other values (20)68
11.9%

_embedded_show_genres
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct21
Distinct (%)25.6%
Missing0
Missing (%)0.0%
Memory size784.0 B
[]
23 
['Drama', 'Romance']
13 
['Crime']
12 
['Comedy']
['Drama', 'Romance', 'History']
Other values (16)
23 

Length

Max length33
Median length31
Mean length15.20731707
Min length2

Characters and Unicode

Total characters1247
Distinct characters31
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)12.2%

Sample

1st row['Drama', 'Crime', 'Mystery']
2nd row[]
3rd row['Drama', 'Adventure', 'Mystery']
4th row['Drama', 'Adventure', 'Mystery']
5th row[]

Common Values

ValueCountFrequency (%)
[]23
28.0%
['Drama', 'Romance']13
15.9%
['Crime']12
14.6%
['Comedy']7
 
8.5%
['Drama', 'Romance', 'History']4
 
4.9%
['Adventure', 'Anime', 'Fantasy']3
 
3.7%
['Crime', 'Thriller', 'Mystery']2
 
2.4%
['Drama', 'Adventure', 'Mystery']2
 
2.4%
['Drama', 'Fantasy']2
 
2.4%
['Drama', 'Comedy', 'Romance']2
 
2.4%
Other values (11)12
14.6%

Length

2022-05-09T21:18:32.719614image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
drama31
21.8%
23
16.2%
romance21
14.8%
crime18
12.7%
comedy11
 
7.7%
adventure6
 
4.2%
fantasy6
 
4.2%
thriller5
 
3.5%
mystery5
 
3.5%
history4
 
2.8%
Other values (6)12
 
8.5%

Most occurring characters

ValueCountFrequency (%)
'238
19.1%
a96
 
7.7%
m86
 
6.9%
[82
 
6.6%
]82
 
6.6%
e77
 
6.2%
r76
 
6.1%
,60
 
4.8%
60
 
4.8%
o43
 
3.4%
Other values (21)347
27.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter606
48.6%
Other Punctuation298
23.9%
Uppercase Letter119
 
9.5%
Open Punctuation82
 
6.6%
Close Punctuation82
 
6.6%
Space Separator60
 
4.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a96
15.8%
m86
14.2%
e77
12.7%
r76
12.5%
o43
7.1%
n42
6.9%
i37
 
6.1%
y32
 
5.3%
t26
 
4.3%
c25
 
4.1%
Other values (7)66
10.9%
Uppercase Letter
ValueCountFrequency (%)
D31
26.1%
C30
25.2%
R21
17.6%
A14
11.8%
F8
 
6.7%
T5
 
4.2%
M5
 
4.2%
H4
 
3.4%
S1
 
0.8%
Other Punctuation
ValueCountFrequency (%)
'238
79.9%
,60
 
20.1%
Open Punctuation
ValueCountFrequency (%)
[82
100.0%
Close Punctuation
ValueCountFrequency (%)
]82
100.0%
Space Separator
ValueCountFrequency (%)
60
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin725
58.1%
Common522
41.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a96
13.2%
m86
11.9%
e77
10.6%
r76
10.5%
o43
 
5.9%
n42
 
5.8%
i37
 
5.1%
y32
 
4.4%
D31
 
4.3%
C30
 
4.1%
Other values (16)175
24.1%
Common
ValueCountFrequency (%)
'238
45.6%
[82
 
15.7%
]82
 
15.7%
,60
 
11.5%
60
 
11.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1247
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
'238
19.1%
a96
 
7.7%
m86
 
6.9%
[82
 
6.6%
]82
 
6.6%
e77
 
6.2%
r76
 
6.1%
,60
 
4.8%
60
 
4.8%
o43
 
3.4%
Other values (21)347
27.8%

_embedded_show_status
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size784.0 B
Running
44 
Ended
32 
To Be Determined

Length

Max length16
Median length7
Mean length6.87804878
Min length5

Characters and Unicode

Total characters564
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEnded
2nd rowEnded
3rd rowEnded
4th rowEnded
5th rowEnded

Common Values

ValueCountFrequency (%)
Running44
53.7%
Ended32
39.0%
To Be Determined6
 
7.3%

Length

2022-05-09T21:18:32.808811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:18:32.935352image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
running44
46.8%
ended32
34.0%
to6
 
6.4%
be6
 
6.4%
determined6
 
6.4%

Most occurring characters

ValueCountFrequency (%)
n170
30.1%
d70
12.4%
e56
 
9.9%
i50
 
8.9%
R44
 
7.8%
u44
 
7.8%
g44
 
7.8%
E32
 
5.7%
12
 
2.1%
T6
 
1.1%
Other values (6)36
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter458
81.2%
Uppercase Letter94
 
16.7%
Space Separator12
 
2.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n170
37.1%
d70
15.3%
e56
 
12.2%
i50
 
10.9%
u44
 
9.6%
g44
 
9.6%
o6
 
1.3%
t6
 
1.3%
r6
 
1.3%
m6
 
1.3%
Uppercase Letter
ValueCountFrequency (%)
R44
46.8%
E32
34.0%
T6
 
6.4%
B6
 
6.4%
D6
 
6.4%
Space Separator
ValueCountFrequency (%)
12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin552
97.9%
Common12
 
2.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n170
30.8%
d70
12.7%
e56
 
10.1%
i50
 
9.1%
R44
 
8.0%
u44
 
8.0%
g44
 
8.0%
E32
 
5.8%
T6
 
1.1%
o6
 
1.1%
Other values (5)30
 
5.4%
Common
ValueCountFrequency (%)
12
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII564
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n170
30.1%
d70
12.4%
e56
 
9.9%
i50
 
8.9%
R44
 
7.8%
u44
 
7.8%
g44
 
7.8%
E32
 
5.7%
12
 
2.1%
T6
 
1.1%
Other values (6)36
 
6.4%

_embedded_show_runtime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct23
Distinct (%)30.3%
Missing6
Missing (%)7.3%
Infinite0
Infinite (%)0.0%
Mean38.38157895
Minimum2
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size784.0 B
2022-05-09T21:18:33.030637image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4.75
Q118.75
median30
Q348
95-th percentile120
Maximum180
Range178
Interquartile range (IQR)29.25

Descriptive statistics

Standard deviation33.40337991
Coefficient of variation (CV)0.8702971797
Kurtosis4.734263691
Mean38.38157895
Median Absolute Deviation (MAD)15
Skewness1.978268747
Sum2917
Variance1115.785789
MonotonicityNot monotonic
2022-05-09T21:18:33.115160image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2712
14.6%
309
11.0%
459
11.0%
606
 
7.3%
75
 
6.1%
1204
 
4.9%
483
 
3.7%
153
 
3.7%
503
 
3.7%
203
 
3.7%
Other values (13)19
23.2%
(Missing)6
 
7.3%
ValueCountFrequency (%)
22
 
2.4%
42
 
2.4%
52
 
2.4%
75
6.1%
81
 
1.2%
103
3.7%
121
 
1.2%
153
3.7%
203
3.7%
221
 
1.2%
ValueCountFrequency (%)
1801
 
1.2%
1301
 
1.2%
1204
4.9%
901
 
1.2%
606
7.3%
512
 
2.4%
503
 
3.7%
483
 
3.7%
459
11.0%
309
11.0%

_embedded_show_averageRuntime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct28
Distinct (%)34.6%
Missing1
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean36.80246914
Minimum2
Maximum181
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size784.0 B
2022-05-09T21:18:33.224956image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q115
median27
Q345
95-th percentile120
Maximum181
Range179
Interquartile range (IQR)30

Descriptive statistics

Standard deviation32.43663814
Coefficient of variation (CV)0.8813712477
Kurtosis5.324969945
Mean36.80246914
Median Absolute Deviation (MAD)18
Skewness2.054494069
Sum2981
Variance1052.135494
MonotonicityNot monotonic
2022-05-09T21:18:33.319245image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
2712
14.6%
459
 
11.0%
307
 
8.5%
605
 
6.1%
75
 
6.1%
254
 
4.9%
153
 
3.7%
1203
 
3.7%
483
 
3.7%
203
 
3.7%
Other values (18)27
32.9%
ValueCountFrequency (%)
22
 
2.4%
42
 
2.4%
52
 
2.4%
75
6.1%
92
 
2.4%
103
3.7%
111
 
1.2%
122
 
2.4%
141
 
1.2%
153
3.7%
ValueCountFrequency (%)
1811
 
1.2%
1301
 
1.2%
1203
3.7%
971
 
1.2%
901
 
1.2%
771
 
1.2%
605
6.1%
503
3.7%
483
3.7%
471
 
1.2%

_embedded_show_premiered
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct44
Distinct (%)53.7%
Missing0
Missing (%)0.0%
Memory size784.0 B
2020-12-21
19 
2020-12-14
2020-11-23
2020-12-07
 
4
2020-11-30
 
2
Other values (39)
44 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters820
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)41.5%

Sample

1st row2018-10-29
2nd row2020-05-11
3rd row2020-12-07
4th row2020-12-07
5th row2020-12-17

Common Values

ValueCountFrequency (%)
2020-12-2119
23.2%
2020-12-148
 
9.8%
2020-11-235
 
6.1%
2020-12-074
 
4.9%
2020-11-302
 
2.4%
2011-09-282
 
2.4%
2020-12-202
 
2.4%
2013-12-242
 
2.4%
2020-11-192
 
2.4%
2020-12-132
 
2.4%
Other values (34)34
41.5%

Length

2022-05-09T21:18:33.540488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-12-2119
23.2%
2020-12-148
 
9.8%
2020-11-235
 
6.1%
2020-12-074
 
4.9%
2013-12-242
 
2.4%
2020-12-132
 
2.4%
2020-11-192
 
2.4%
2020-12-202
 
2.4%
2011-09-282
 
2.4%
2020-11-302
 
2.4%
Other values (34)34
41.5%

Most occurring characters

ValueCountFrequency (%)
2216
26.3%
0190
23.2%
-164
20.0%
1153
18.7%
920
 
2.4%
419
 
2.3%
319
 
2.3%
813
 
1.6%
712
 
1.5%
59
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number656
80.0%
Dash Punctuation164
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2216
32.9%
0190
29.0%
1153
23.3%
920
 
3.0%
419
 
2.9%
319
 
2.9%
813
 
2.0%
712
 
1.8%
59
 
1.4%
65
 
0.8%
Dash Punctuation
ValueCountFrequency (%)
-164
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common820
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2216
26.3%
0190
23.2%
-164
20.0%
1153
18.7%
920
 
2.4%
419
 
2.3%
319
 
2.3%
813
 
1.6%
712
 
1.5%
59
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII820
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2216
26.3%
0190
23.2%
-164
20.0%
1153
18.7%
920
 
2.4%
419
 
2.3%
319
 
2.3%
813
 
1.6%
712
 
1.5%
59
 
1.1%

_embedded_show_ended
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct17
Distinct (%)20.7%
Missing0
Missing (%)0.0%
Memory size784.0 B
nan
50 
2021-01-18
2020-12-28
 
3
2021-01-25
 
3
2020-12-23
 
2
Other values (12)
17 

Length

Max length10
Median length3
Mean length5.731707317
Min length3

Characters and Unicode

Total characters470
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)8.5%

Sample

1st row2021-01-07
2nd row2021-02-08
3rd row2020-12-28
4th row2020-12-28
5th row2020-12-25

Common Values

ValueCountFrequency (%)
nan50
61.0%
2021-01-187
 
8.5%
2020-12-283
 
3.7%
2021-01-253
 
3.7%
2020-12-232
 
2.4%
2020-12-312
 
2.4%
2020-12-222
 
2.4%
2021-01-052
 
2.4%
2020-12-212
 
2.4%
2020-12-302
 
2.4%
Other values (7)7
 
8.5%

Length

2022-05-09T21:18:33.618964image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nan50
61.0%
2021-01-187
 
8.5%
2020-12-283
 
3.7%
2021-01-253
 
3.7%
2021-01-052
 
2.4%
2020-12-302
 
2.4%
2020-12-212
 
2.4%
2020-12-222
 
2.4%
2020-12-312
 
2.4%
2020-12-232
 
2.4%
Other values (7)7
 
8.5%

Most occurring characters

ValueCountFrequency (%)
2101
21.5%
n100
21.3%
071
15.1%
-64
13.6%
156
11.9%
a50
10.6%
811
 
2.3%
56
 
1.3%
36
 
1.3%
62
 
0.4%
Other values (2)3
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number256
54.5%
Lowercase Letter150
31.9%
Dash Punctuation64
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2101
39.5%
071
27.7%
156
21.9%
811
 
4.3%
56
 
2.3%
36
 
2.3%
62
 
0.8%
72
 
0.8%
41
 
0.4%
Lowercase Letter
ValueCountFrequency (%)
n100
66.7%
a50
33.3%
Dash Punctuation
ValueCountFrequency (%)
-64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common320
68.1%
Latin150
31.9%

Most frequent character per script

Common
ValueCountFrequency (%)
2101
31.6%
071
22.2%
-64
20.0%
156
17.5%
811
 
3.4%
56
 
1.9%
36
 
1.9%
62
 
0.6%
72
 
0.6%
41
 
0.3%
Latin
ValueCountFrequency (%)
n100
66.7%
a50
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII470
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2101
21.5%
n100
21.3%
071
15.1%
-64
13.6%
156
11.9%
a50
10.6%
811
 
2.3%
56
 
1.3%
36
 
1.3%
62
 
0.4%
Other values (2)3
 
0.6%

_embedded_show_officialSite
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct51
Distinct (%)62.2%
Missing0
Missing (%)0.0%
Memory size784.0 B
https://www.iqiyi.com/a_c4m3iuc94t.html
12 
nan
https://programme.mytvsuper.com/tc/130336/
 
5
https://v.qq.com/detail/m/mzc002007995z4v.html
 
3
https://roosterteeth.com/series/rt-animated-adventures
 
2
Other values (46)
53 

Length

Max length105
Median length77
Mean length44.7195122
Min length3

Characters and Unicode

Total characters3667
Distinct characters75
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)47.6%

Sample

1st rowhttps://premier.one/show/8405
2nd rowhttps://premier.one/collections/134
3rd rowhttps://premier.one/show/12339
4th rowhttps://premier.one/show/12339
5th rowhttps://www.ivi.ru/watch/muzhskaya-tema

Common Values

ValueCountFrequency (%)
https://www.iqiyi.com/a_c4m3iuc94t.html12
 
14.6%
nan7
 
8.5%
https://programme.mytvsuper.com/tc/130336/5
 
6.1%
https://v.qq.com/detail/m/mzc002007995z4v.html3
 
3.7%
https://roosterteeth.com/series/rt-animated-adventures2
 
2.4%
https://v.qq.com/detail/m/mzc00200dnvb1wh.html2
 
2.4%
https://so.youku.com/search_video/q_%20%E6%9C%80%E5%88%9D%E7%9A%84%E7%9B%B8%E9%81%87?searchfrom=12
 
2.4%
https://v.qq.com/x/search/?q=+%E4%BB%8A%E5%A4%95%E4%BD%95%E5%A4%95&stag=0&smartbox_ab=2
 
2.4%
https://viaplay.no/serier/professionals2
 
2.4%
https://www.tytnetwork.com2
 
2.4%
Other values (41)43
52.4%

Length

2022-05-09T21:18:33.729505image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.iqiyi.com/a_c4m3iuc94t.html12
 
14.6%
nan7
 
8.5%
https://programme.mytvsuper.com/tc/1303365
 
6.1%
https://v.qq.com/detail/m/mzc002007995z4v.html3
 
3.7%
https://viaplay.no/serier/professionals2
 
2.4%
https://v.youku.com/v_show/id_xndk4otuxmzg1mg==.html?spm=a2hbt.13141534.0.13141534&s=6eefbfbd4befbfbd32ef2
 
2.4%
https://www.tytnetwork.com2
 
2.4%
https://premier.one/show/123392
 
2.4%
https://v.qq.com/x/search/?q=+%e4%bb%8a%e5%a4%95%e4%bd%95%e5%a4%95&stag=0&smartbox_ab2
 
2.4%
https://so.youku.com/search_video/q_%20%e6%9c%80%e5%88%9d%e7%9a%84%e7%9b%b8%e9%81%87?searchfrom=12
 
2.4%
Other values (41)43
52.4%

Most occurring characters

ValueCountFrequency (%)
/299
 
8.2%
t296
 
8.1%
s174
 
4.7%
.162
 
4.4%
e162
 
4.4%
o155
 
4.2%
m147
 
4.0%
h147
 
4.0%
w139
 
3.8%
c128
 
3.5%
Other values (65)1858
50.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2382
65.0%
Other Punctuation616
 
16.8%
Decimal Number394
 
10.7%
Uppercase Letter182
 
5.0%
Dash Punctuation41
 
1.1%
Connector Punctuation27
 
0.7%
Math Symbol25
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t296
 
12.4%
s174
 
7.3%
e162
 
6.8%
o155
 
6.5%
m147
 
6.2%
h147
 
6.2%
w139
 
5.8%
c128
 
5.4%
p123
 
5.2%
i121
 
5.1%
Other values (16)790
33.2%
Uppercase Letter
ValueCountFrequency (%)
E22
 
12.1%
B15
 
8.2%
P13
 
7.1%
C12
 
6.6%
D11
 
6.0%
U11
 
6.0%
A10
 
5.5%
M9
 
4.9%
S8
 
4.4%
J7
 
3.8%
Other values (16)64
35.2%
Decimal Number
ValueCountFrequency (%)
462
15.7%
056
14.2%
354
13.7%
946
11.7%
144
11.2%
234
8.6%
531
7.9%
824
 
6.1%
622
 
5.6%
721
 
5.3%
Other Punctuation
ValueCountFrequency (%)
/299
48.5%
.162
26.3%
:75
 
12.2%
%57
 
9.3%
?11
 
1.8%
&8
 
1.3%
,2
 
0.3%
#1
 
0.2%
!1
 
0.2%
Math Symbol
ValueCountFrequency (%)
=23
92.0%
+2
 
8.0%
Dash Punctuation
ValueCountFrequency (%)
-41
100.0%
Connector Punctuation
ValueCountFrequency (%)
_27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2564
69.9%
Common1103
30.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
t296
 
11.5%
s174
 
6.8%
e162
 
6.3%
o155
 
6.0%
m147
 
5.7%
h147
 
5.7%
w139
 
5.4%
c128
 
5.0%
p123
 
4.8%
i121
 
4.7%
Other values (42)972
37.9%
Common
ValueCountFrequency (%)
/299
27.1%
.162
14.7%
:75
 
6.8%
462
 
5.6%
%57
 
5.2%
056
 
5.1%
354
 
4.9%
946
 
4.2%
144
 
4.0%
-41
 
3.7%
Other values (13)207
18.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII3667
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/299
 
8.2%
t296
 
8.1%
s174
 
4.7%
.162
 
4.4%
e162
 
4.4%
o155
 
4.2%
m147
 
4.0%
h147
 
4.0%
w139
 
3.8%
c128
 
3.5%
Other values (65)1858
50.7%

_embedded_show_weight
Real number (ℝ≥0)

HIGH CORRELATION

Distinct39
Distinct (%)47.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.56097561
Minimum2
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size784.0 B
2022-05-09T21:18:33.860029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6
Q118.5
median30
Q343.5
95-th percentile84.9
Maximum97
Range95
Interquartile range (IQR)25

Descriptive statistics

Standard deviation23.73729553
Coefficient of variation (CV)0.649252246
Kurtosis0.1492099988
Mean36.56097561
Median Absolute Deviation (MAD)12
Skewness0.947679119
Sum2998
Variance563.459199
MonotonicityNot monotonic
2022-05-09T21:18:33.975136image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
3412
 
14.6%
158
 
9.8%
254
 
4.9%
654
 
4.9%
304
 
4.9%
63
 
3.7%
273
 
3.7%
283
 
3.7%
173
 
3.7%
293
 
3.7%
Other values (29)35
42.7%
ValueCountFrequency (%)
22
 
2.4%
31
 
1.2%
63
 
3.7%
101
 
1.2%
141
 
1.2%
158
9.8%
173
 
3.7%
182
 
2.4%
201
 
1.2%
222
 
2.4%
ValueCountFrequency (%)
972
2.4%
941
1.2%
871
1.2%
851
1.2%
831
1.2%
771
1.2%
751
1.2%
731
1.2%
721
1.2%
672
2.4%

_embedded_show_dvdCountry
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size784.0 B
nan
82 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters246
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th rownan

Common Values

ValueCountFrequency (%)
nan82
100.0%

Length

2022-05-09T21:18:34.077501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:18:34.196537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
nan82
100.0%

Most occurring characters

ValueCountFrequency (%)
n164
66.7%
a82
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter246
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n164
66.7%
a82
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin246
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n164
66.7%
a82
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII246
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n164
66.7%
a82
33.3%

_embedded_show_summary
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct50
Distinct (%)61.0%
Missing0
Missing (%)0.0%
Memory size784.0 B
<p>The play is set in the turbulent period of the Republic of China in Shanghai. In a turbulent era, the forensic doctor Che Suwei and the gentleman detective Gu Yuan are intertwined with various forces. "Deputy Inspector Kang Yichen, and the innocent and lively reporter Cao Qingluo worked together to crack out a number of weird and curious cases, and restore the truth.</p>
12 
nan
11 
<p>At the end of the calendar 2020, the continent of Stern, which has reached the end of civilization due to the exhaustion of magic elements, ushered in the destruction of the continent under the void storm. Ye Xuan, the last god of law in the mainland, unexpectedly awakened in the era of the prosperous magic civilization three thousand years ago and became an ordinary student at the Sith Magic Academy on the border of the Kingdom of Orlando in the northwest of the mainland. In order to save the mainland and prevent the end from coming, Ye Xuan began to explore the mystery of the dark turmoil that led to the depletion of magical elements in the mainland three thousand years ago, to prevent the mainland crisis.</p>
 
3
<p>During the Yin Dynasty, Dong Yue, a brave general in the Dingyuan Rebellion, was sent back in time to stop a war that would claim the lives of countless innocents. She sets out to murder corrupted officer Lu Yuantong in an attempt to prevent war, and during her journey she met Feng Xi and Pang Yu. Pang Yu and Feng Xi were old friends who cared deeply for each other, but fell out and turn into enemies. While trying to reconcile the two brothers, Dong Yue also tries to stop Lu Yuantang's evil schemes which are poised to tear the nation apart with their help.</p>
 
2
<p>The stories from the popular Rooster Teeth Podcast now in animated form!</p>
 
2
Other values (45)
52 

Length

Max length913
Median length584
Mean length327.9878049
Min length3

Characters and Unicode

Total characters26895
Distinct characters95
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)46.3%

Sample

1st row<p>Marina is in her late 30s, she has a successful business and a close-knit family. Her husband is a surgeon and her daughters study at fancy establishments. To everybody her life seems perfect. Though, it is all just a facade concealing the real problems: her husband has a mistress, her elder daughter is a slacker and drug-dealer, her youngest is a sociopath. Well, Marina herself is not really a flower-lady, but a brothel-keeper who is hiding her dark business from everyone. The truth may come out when a girl of Marina's is found dead.</p>
2nd row<p>This is not an interview, this is a confession. Revelations of the artist in the form of a monologue. The guest's opinion may not coincide with the opinion of the PREMIER platform editorial board.</p>
3rd rownan
4th rownan
5th row<p><b>Мужская тема</b> is a symbiosis of talk shows and modern podcasts, where male celebrities answer questions that concern people in the XXI century. Bright representatives of show business, theater, pop, cinema, sports, as well as Internet stars meet in the barbershop. Here, on male territory, they can openly discuss a variety of topics, sometimes seriously, and sometimes with humor. This is a chance to see the idol in a confidential communication without notes, compare his opinion with your own and hear what men really talk about when there is not a single girl around.</p>

Common Values

ValueCountFrequency (%)
<p>The play is set in the turbulent period of the Republic of China in Shanghai. In a turbulent era, the forensic doctor Che Suwei and the gentleman detective Gu Yuan are intertwined with various forces. "Deputy Inspector Kang Yichen, and the innocent and lively reporter Cao Qingluo worked together to crack out a number of weird and curious cases, and restore the truth.</p>12
 
14.6%
nan11
 
13.4%
<p>At the end of the calendar 2020, the continent of Stern, which has reached the end of civilization due to the exhaustion of magic elements, ushered in the destruction of the continent under the void storm. Ye Xuan, the last god of law in the mainland, unexpectedly awakened in the era of the prosperous magic civilization three thousand years ago and became an ordinary student at the Sith Magic Academy on the border of the Kingdom of Orlando in the northwest of the mainland. In order to save the mainland and prevent the end from coming, Ye Xuan began to explore the mystery of the dark turmoil that led to the depletion of magical elements in the mainland three thousand years ago, to prevent the mainland crisis.</p>3
 
3.7%
<p>During the Yin Dynasty, Dong Yue, a brave general in the Dingyuan Rebellion, was sent back in time to stop a war that would claim the lives of countless innocents. She sets out to murder corrupted officer Lu Yuantong in an attempt to prevent war, and during her journey she met Feng Xi and Pang Yu. Pang Yu and Feng Xi were old friends who cared deeply for each other, but fell out and turn into enemies. While trying to reconcile the two brothers, Dong Yue also tries to stop Lu Yuantang's evil schemes which are poised to tear the nation apart with their help.</p>2
 
2.4%
<p>The stories from the popular Rooster Teeth Podcast now in animated form!</p>2
 
2.4%
<p>A daring, funny, and brutally honest show that covers politics, entertainment, movies, sports, and pop culture.</p>2
 
2.4%
<p>A story that follows two people's brave pursuit of love from their campus days to their humble beginnings as they enter the workplace to chase after their dreams together.</p>2
 
2.4%
<p>The play consists of three youth stories. "He and Meow": The cat Jiang Xiao Kui and his owner Jiang Qing from the cat kingdom live a happy life. Until Jiang Qing's younger brother Jiang Xia returned home. Xia, who was allergic to cats, and Jiang Xiao Kui, who hated his younger brother, started a battle over sister's favor. "Full-time rival": Xu Tian Yi and Li Shi Lin, who had been at odds for a long time, reunited during the summer sprint training. In the process of competing against each other, their misunderstanding was resolved. Just when the two worked together to enter the team, an accident happened. "The Man in the Story": Yu Sheng, a young man, accidentally discovered that he turned out to be a character in Xu Mo's novel. After learning about the tragic ending of himself and his sister, he came to the real world to fight with the writer in an attempt to change his destiny.</p><p><br /> </p>2
 
2.4%
<p>Ju Xuanwen (Wan Yan Lo-yun) is a man with a noble appearance and many virtues. It is a pity that he fell ill with neurosis at a young age - after an unexplained car accident he falls into a delusional state and considers himself a prince. Since then, he no longer cares about the activities of his company and concentrates on becoming emperor.<br />Lo Huai (Chuang Da Fei) - psychiatrist on the verge of bankruptcy. Because of the need for money, she took responsibility for the treatment of Ju Xuanwen. However, she did not expect her peaceful days to end one day. Spending time together, they began to fall in love with each other.</p>2
 
2.4%
<p>The disciples of the Lingchuan Sect have guarded the Fans of Heaven and Earth for nearly a century. Mu Yun and Hua Yue are the only disciples of the sect that are left. The stubborn and disobedient Hua Yue unintentionally discovers that the Fan of Heaven possesses the power to travel through time. To escape being forced to study and practice martial arts by Mu Yun, Hua Yue travels to the future to have fun. Hundreds of years in the future she meets Xiao Qian who looks exactly like her. Secrets come to the surface, and adventures take place.</p>2
 
2.4%
Other values (40)42
51.2%

Length

2022-05-09T21:18:34.291889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the315
 
7.0%
and181
 
4.0%
of161
 
3.6%
a131
 
2.9%
to128
 
2.8%
in124
 
2.8%
with52
 
1.2%
is48
 
1.1%
that36
 
0.8%
are27
 
0.6%
Other values (1315)3293
73.2%

Most occurring characters

ValueCountFrequency (%)
4404
16.4%
e2605
 
9.7%
t1873
 
7.0%
a1712
 
6.4%
n1684
 
6.3%
o1552
 
5.8%
i1414
 
5.3%
r1340
 
5.0%
s1140
 
4.2%
h1048
 
3.9%
Other values (85)8123
30.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter20367
75.7%
Space Separator4414
 
16.4%
Uppercase Letter904
 
3.4%
Other Punctuation682
 
2.5%
Math Symbol404
 
1.5%
Decimal Number62
 
0.2%
Dash Punctuation41
 
0.2%
Open Punctuation10
 
< 0.1%
Close Punctuation10
 
< 0.1%
Currency Symbol1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e2605
12.8%
t1873
 
9.2%
a1712
 
8.4%
n1684
 
8.3%
o1552
 
7.6%
i1414
 
6.9%
r1340
 
6.6%
s1140
 
5.6%
h1048
 
5.1%
d772
 
3.8%
Other values (30)5227
25.7%
Uppercase Letter
ValueCountFrequency (%)
S90
 
10.0%
T77
 
8.5%
Y76
 
8.4%
C62
 
6.9%
R47
 
5.2%
I44
 
4.9%
W42
 
4.6%
A42
 
4.6%
D41
 
4.5%
M36
 
4.0%
Other values (17)347
38.4%
Other Punctuation
ValueCountFrequency (%)
,252
37.0%
.218
32.0%
/104
15.2%
"46
 
6.7%
'41
 
6.0%
:10
 
1.5%
!7
 
1.0%
?2
 
0.3%
&1
 
0.1%
;1
 
0.1%
Decimal Number
ValueCountFrequency (%)
015
24.2%
214
22.6%
111
17.7%
97
11.3%
35
 
8.1%
83
 
4.8%
53
 
4.8%
72
 
3.2%
62
 
3.2%
Space Separator
ValueCountFrequency (%)
4404
99.8%
 10
 
0.2%
Math Symbol
ValueCountFrequency (%)
<202
50.0%
>202
50.0%
Dash Punctuation
ValueCountFrequency (%)
-33
80.5%
8
 
19.5%
Open Punctuation
ValueCountFrequency (%)
(10
100.0%
Close Punctuation
ValueCountFrequency (%)
)10
100.0%
Currency Symbol
ValueCountFrequency (%)
$1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin21260
79.0%
Common5624
 
20.9%
Cyrillic11
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e2605
12.3%
t1873
 
8.8%
a1712
 
8.1%
n1684
 
7.9%
o1552
 
7.3%
i1414
 
6.7%
r1340
 
6.3%
s1140
 
5.4%
h1048
 
4.9%
d772
 
3.6%
Other values (47)6120
28.8%
Common
ValueCountFrequency (%)
4404
78.3%
,252
 
4.5%
.218
 
3.9%
<202
 
3.6%
>202
 
3.6%
/104
 
1.8%
"46
 
0.8%
'41
 
0.7%
-33
 
0.6%
015
 
0.3%
Other values (18)107
 
1.9%
Cyrillic
ValueCountFrequency (%)
а2
18.2%
я1
9.1%
м1
9.1%
е1
9.1%
т1
9.1%
у1
9.1%
к1
9.1%
с1
9.1%
ж1
9.1%
М1
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII26859
99.9%
None17
 
0.1%
Cyrillic11
 
< 0.1%
Punctuation8
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4404
16.4%
e2605
 
9.7%
t1873
 
7.0%
a1712
 
6.4%
n1684
 
6.3%
o1552
 
5.8%
i1414
 
5.3%
r1340
 
5.0%
s1140
 
4.2%
h1048
 
3.9%
Other values (68)8087
30.1%
None
ValueCountFrequency (%)
 10
58.8%
é3
 
17.6%
å1
 
5.9%
ı1
 
5.9%
ç1
 
5.9%
ā1
 
5.9%
Punctuation
ValueCountFrequency (%)
8
100.0%
Cyrillic
ValueCountFrequency (%)
а2
18.2%
я1
9.1%
м1
9.1%
е1
9.1%
т1
9.1%
у1
9.1%
к1
9.1%
с1
9.1%
ж1
9.1%
М1
9.1%

_embedded_show_updated
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct55
Distinct (%)67.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1631578281
Minimum1608523612
Maximum1652004708
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size784.0 B
2022-05-09T21:18:34.422263image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1608523612
5-th percentile1609118201
Q11611538948
median1640144118
Q31648190058
95-th percentile1651776644
Maximum1652004708
Range43481096
Interquartile range (IQR)36651110

Descriptive statistics

Standard deviation17723640.28
Coefficient of variation (CV)0.01086288073
Kurtosis-1.790094124
Mean1631578281
Median Absolute Deviation (MAD)11424672
Skewness-0.249663216
Sum1.337894191 × 1011
Variance3.141274248 × 1014
MonotonicityNot monotonic
2022-05-09T21:18:34.532071image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
160911820112
 
14.6%
16115389485
 
6.1%
16426893193
 
3.7%
16492624392
 
2.4%
16124781452
 
2.4%
16090607262
 
2.4%
16095351412
 
2.4%
16154510692
 
2.4%
16520047082
 
2.4%
16481900582
 
2.4%
Other values (45)48
58.5%
ValueCountFrequency (%)
16085236121
 
1.2%
16090607262
 
2.4%
160911820112
14.6%
16095351412
 
2.4%
16101108411
 
1.2%
16114368421
 
1.2%
16115389485
6.1%
16117257131
 
1.2%
16124781452
 
2.4%
16130883481
 
1.2%
ValueCountFrequency (%)
16520047082
2.4%
16519332091
1.2%
16518386471
1.2%
16517773161
1.2%
16517638721
1.2%
16515703161
1.2%
16515672641
1.2%
16515025931
1.2%
16514293371
1.2%
16509836761
1.2%

_links_self_href
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct82
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size784.0 B
https://api.tvmaze.com/episodes/1977902
 
1
https://api.tvmaze.com/episodes/1998676
 
1
https://api.tvmaze.com/episodes/1998674
 
1
https://api.tvmaze.com/episodes/1998673
 
1
https://api.tvmaze.com/episodes/1997815
 
1
Other values (77)
77 

Length

Max length39
Median length39
Mean length39
Min length39

Characters and Unicode

Total characters3198
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique82 ?
Unique (%)100.0%

Sample

1st rowhttps://api.tvmaze.com/episodes/1977902
2nd rowhttps://api.tvmaze.com/episodes/2015818
3rd rowhttps://api.tvmaze.com/episodes/1964000
4th rowhttps://api.tvmaze.com/episodes/1995405
5th rowhttps://api.tvmaze.com/episodes/2007760

Common Values

ValueCountFrequency (%)
https://api.tvmaze.com/episodes/19779021
 
1.2%
https://api.tvmaze.com/episodes/19986761
 
1.2%
https://api.tvmaze.com/episodes/19986741
 
1.2%
https://api.tvmaze.com/episodes/19986731
 
1.2%
https://api.tvmaze.com/episodes/19978151
 
1.2%
https://api.tvmaze.com/episodes/19978141
 
1.2%
https://api.tvmaze.com/episodes/20833311
 
1.2%
https://api.tvmaze.com/episodes/19503691
 
1.2%
https://api.tvmaze.com/episodes/20927291
 
1.2%
https://api.tvmaze.com/episodes/19967981
 
1.2%
Other values (72)72
87.8%

Length

2022-05-09T21:18:34.638768image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://api.tvmaze.com/episodes/19779021
 
1.2%
https://api.tvmaze.com/episodes/19986091
 
1.2%
https://api.tvmaze.com/episodes/19954051
 
1.2%
https://api.tvmaze.com/episodes/20077601
 
1.2%
https://api.tvmaze.com/episodes/19857891
 
1.2%
https://api.tvmaze.com/episodes/20396221
 
1.2%
https://api.tvmaze.com/episodes/20396231
 
1.2%
https://api.tvmaze.com/episodes/23244271
 
1.2%
https://api.tvmaze.com/episodes/23244281
 
1.2%
https://api.tvmaze.com/episodes/23244291
 
1.2%
Other values (72)72
87.8%

Most occurring characters

ValueCountFrequency (%)
/328
 
10.3%
p246
 
7.7%
s246
 
7.7%
e246
 
7.7%
t246
 
7.7%
o164
 
5.1%
a164
 
5.1%
i164
 
5.1%
.164
 
5.1%
m164
 
5.1%
Other values (16)1066
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2050
64.1%
Other Punctuation574
 
17.9%
Decimal Number574
 
17.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p246
12.0%
s246
12.0%
e246
12.0%
t246
12.0%
o164
8.0%
a164
8.0%
i164
8.0%
m164
8.0%
h82
 
4.0%
d82
 
4.0%
Other values (3)246
12.0%
Decimal Number
ValueCountFrequency (%)
9104
18.1%
290
15.7%
177
13.4%
355
9.6%
050
8.7%
849
8.5%
641
 
7.1%
440
 
7.0%
736
 
6.3%
532
 
5.6%
Other Punctuation
ValueCountFrequency (%)
/328
57.1%
.164
28.6%
:82
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin2050
64.1%
Common1148
35.9%

Most frequent character per script

Common
ValueCountFrequency (%)
/328
28.6%
.164
14.3%
9104
 
9.1%
290
 
7.8%
:82
 
7.1%
177
 
6.7%
355
 
4.8%
050
 
4.4%
849
 
4.3%
641
 
3.6%
Other values (3)108
 
9.4%
Latin
ValueCountFrequency (%)
p246
12.0%
s246
12.0%
e246
12.0%
t246
12.0%
o164
8.0%
a164
8.0%
i164
8.0%
m164
8.0%
h82
 
4.0%
d82
 
4.0%
Other values (3)246
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3198
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/328
 
10.3%
p246
 
7.7%
s246
 
7.7%
e246
 
7.7%
t246
 
7.7%
o164
 
5.1%
a164
 
5.1%
i164
 
5.1%
.164
 
5.1%
m164
 
5.1%
Other values (16)1066
33.3%

Interactions

2022-05-09T21:18:26.646031image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:06.171539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:11.428247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:13.479115image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:15.356464image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:17.521798image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:20.920685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:22.762557image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:24.677586image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:27.428100image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:07.730170image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:12.132609image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:14.181422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:16.121534image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:18.405362image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:21.750281image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:23.518572image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:25.504360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:27.529616image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:08.214262image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:12.232207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:14.284872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:16.227423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:18.652158image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:21.856062image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:23.627758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:25.599294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:27.621331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:08.573227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:12.351171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:14.385853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:16.341517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:18.883442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:21.949949image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:23.728301image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:25.691822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:27.724355image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:08.997737image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:12.460058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:14.485091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:16.478930image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:19.115418image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:22.051130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:23.824256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:25.788329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:28.189829image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:09.856385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:12.934568image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:14.957739image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:16.986869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:19.840661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:22.348587image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:24.257214image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:26.259076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:28.308001image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:10.259482image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:13.040575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:15.053725image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:17.108673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:20.078043image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:22.455276image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:24.362416image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:26.357588image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:28.413996image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:10.614753image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:13.143207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:15.164364image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:17.218261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:20.364627image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:22.570533image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:24.465080image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:26.453668image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:28.510293image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:11.033227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:13.341053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:15.253471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:17.423127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:20.652462image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:22.669065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:24.562755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:18:26.547655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-05-09T21:18:34.717354image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-09T21:18:34.843915image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-09T21:18:34.985322image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-09T21:18:35.149698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-05-09T21:18:35.527233image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-09T21:18:28.788542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-09T21:18:29.509713image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-05-09T21:18:29.722313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-05-09T21:18:29.834770image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

idurlnameseasonnumbertypeairdateairtimeairstampruntimesummary_embedded_show_id_embedded_show_url_embedded_show_name_embedded_show_type_embedded_show_language_embedded_show_genres_embedded_show_status_embedded_show_runtime_embedded_show_averageRuntime_embedded_show_premiered_embedded_show_ended_embedded_show_officialSite_embedded_show_weight_embedded_show_dvdCountry_embedded_show_summary_embedded_show_updated_links_self_href
01977899https://www.tvmaze.com/episodes/1977899/obycnaa-zensina-2x03-seria-12Серия 122.03.0regular2020-12-2110:002020-12-20T22:00:00+00:0055.0nan39115https://www.tvmaze.com/shows/39115/obycnaa-zensinaОбычная женщинаScriptedRussian['Drama', 'Crime', 'Mystery']Ended50.048.02018-10-292021-01-07https://premier.one/show/840536.0nan<p>Marina is in her late 30s, she has a successful business and a close-knit family. Her husband is a surgeon and her daughters study at fancy establishments. To everybody her life seems perfect. Though, it is all just a facade concealing the real problems: her husband has a mistress, her elder daughter is a slacker and drug-dealer, her youngest is a sociopath. Well, Marina herself is not really a flower-lady, but a brothel-keeper who is hiding her dark business from everyone. The truth may come out when a girl of Marina's is found dead.</p>1.610111e+09https://api.tvmaze.com/episodes/1977902
12164195https://www.tvmaze.com/episodes/2164195/ispoved-1x09-viktoria-bonaВиктория Боня1.09.0regular2020-12-2112:002020-12-21T00:00:00+00:0048.0nan48683https://www.tvmaze.com/shows/48683/ispovedИсповедьDocumentaryRussian[]Ended48.047.02020-05-112021-02-08https://premier.one/collections/1342.0nan<p>This is not an interview, this is a confession. Revelations of the artist in the form of a monologue. The guest's opinion may not coincide with the opinion of the PREMIER platform editorial board.</p>1.637062e+09https://api.tvmaze.com/episodes/2015818
21982407https://www.tvmaze.com/episodes/1982407/volk-1x09-seria-09Серия 091.09.0regular2020-12-21nan2020-12-21T00:00:00+00:0051.0nan52181https://www.tvmaze.com/shows/52181/volkВолкScriptedRussian['Drama', 'Adventure', 'Mystery']Ended51.050.02020-12-072020-12-28https://premier.one/show/1233925.0nannan1.640436e+09https://api.tvmaze.com/episodes/1964000
31982408https://www.tvmaze.com/episodes/1982408/volk-1x10-seria-10Серия 101.010.0regular2020-12-21nan2020-12-21T00:00:00+00:0051.0nan52181https://www.tvmaze.com/shows/52181/volkВолкScriptedRussian['Drama', 'Adventure', 'Mystery']Ended51.050.02020-12-072020-12-28https://premier.one/show/1233925.0nannan1.640436e+09https://api.tvmaze.com/episodes/1995405
41988014https://www.tvmaze.com/episodes/1988014/muzskaa-tema-1x03-seria-3Серия 31.03.0regular2020-12-2112:002020-12-21T00:00:00+00:0030.0nan52520https://www.tvmaze.com/shows/52520/muzskaa-temaМужская темаTalk ShowRussian[]Ended30.030.02020-12-172020-12-25https://www.ivi.ru/watch/muzhskaya-tema3.0nan<p><b>Мужская тема</b> is a symbiosis of talk shows and modern podcasts, where male celebrities answer questions that concern people in the XXI century. Bright representatives of show business, theater, pop, cinema, sports, as well as Internet stars meet in the barbershop. Here, on male territory, they can openly discuss a variety of topics, sometimes seriously, and sometimes with humor. This is a chance to see the idol in a confidential communication without notes, compare his opinion with your own and hear what men really talk about when there is not a single girl around.</p>1.616723e+09https://api.tvmaze.com/episodes/2007760
52062926https://www.tvmaze.com/episodes/2062926/god-of-ten-thousand-realms-1x01-episode-1Episode 11.01.0regular2020-12-2110:002020-12-21T02:00:00+00:007.0nan54541https://www.tvmaze.com/shows/54541/god-of-ten-thousand-realmsGod of Ten Thousand RealmsAnimationChinese['Adventure', 'Anime', 'Fantasy']Running7.07.02020-12-21nanhttps://v.qq.com/detail/m/mzc002007995z4v.html65.0nan<p>At the end of the calendar 2020, the continent of Stern, which has reached the end of civilization due to the exhaustion of magic elements, ushered in the destruction of the continent under the void storm. Ye Xuan, the last god of law in the mainland, unexpectedly awakened in the era of the prosperous magic civilization three thousand years ago and became an ordinary student at the Sith Magic Academy on the border of the Kingdom of Orlando in the northwest of the mainland. In order to save the mainland and prevent the end from coming, Ye Xuan began to explore the mystery of the dark turmoil that led to the depletion of magical elements in the mainland three thousand years ago, to prevent the mainland crisis.</p>1.642689e+09https://api.tvmaze.com/episodes/1985789
62062927https://www.tvmaze.com/episodes/2062927/god-of-ten-thousand-realms-1x02-episode-2Episode 21.02.0regular2020-12-2110:002020-12-21T02:00:00+00:007.0nan54541https://www.tvmaze.com/shows/54541/god-of-ten-thousand-realmsGod of Ten Thousand RealmsAnimationChinese['Adventure', 'Anime', 'Fantasy']Running7.07.02020-12-21nanhttps://v.qq.com/detail/m/mzc002007995z4v.html65.0nan<p>At the end of the calendar 2020, the continent of Stern, which has reached the end of civilization due to the exhaustion of magic elements, ushered in the destruction of the continent under the void storm. Ye Xuan, the last god of law in the mainland, unexpectedly awakened in the era of the prosperous magic civilization three thousand years ago and became an ordinary student at the Sith Magic Academy on the border of the Kingdom of Orlando in the northwest of the mainland. In order to save the mainland and prevent the end from coming, Ye Xuan began to explore the mystery of the dark turmoil that led to the depletion of magical elements in the mainland three thousand years ago, to prevent the mainland crisis.</p>1.642689e+09https://api.tvmaze.com/episodes/2039622
72062928https://www.tvmaze.com/episodes/2062928/god-of-ten-thousand-realms-1x03-episode-3Episode 31.03.0regular2020-12-2110:002020-12-21T02:00:00+00:007.0nan54541https://www.tvmaze.com/shows/54541/god-of-ten-thousand-realmsGod of Ten Thousand RealmsAnimationChinese['Adventure', 'Anime', 'Fantasy']Running7.07.02020-12-21nanhttps://v.qq.com/detail/m/mzc002007995z4v.html65.0nan<p>At the end of the calendar 2020, the continent of Stern, which has reached the end of civilization due to the exhaustion of magic elements, ushered in the destruction of the continent under the void storm. Ye Xuan, the last god of law in the mainland, unexpectedly awakened in the era of the prosperous magic civilization three thousand years ago and became an ordinary student at the Sith Magic Academy on the border of the Kingdom of Orlando in the northwest of the mainland. In order to save the mainland and prevent the end from coming, Ye Xuan began to explore the mystery of the dark turmoil that led to the depletion of magical elements in the mainland three thousand years ago, to prevent the mainland crisis.</p>1.642689e+09https://api.tvmaze.com/episodes/2039623
82140388https://www.tvmaze.com/episodes/2140388/going-seventeen-2020-12-21-going-vs-seventeen-2GOING VS SEVENTEEN #22020.043.0regular2020-12-21nan2020-12-21T03:00:00+00:0030.0nan56655https://www.tvmaze.com/shows/56655/going-seventeenGoing SeventeenVarietyKorean[]Running30.030.02017-06-12nannan18.0nan<p>Initially a series of behind-the-scenes vlogs, <b>Going Seventeen</b> has taken a more structured route since mid-2019 and is now a reality-variety show with themed episodes. Every week, the members of Seventeen play games or participate in a variety of activities for everyone's delight and entertainment. Season 2021's keyword is "Watch What You Say", meaning that anything the members say can and will be turned into content...</p>1.651764e+09https://api.tvmaze.com/episodes/2324427
92005748https://www.tvmaze.com/episodes/2005748/legend-of-yun-qian-1x01-episode-1Episode 11.01.0regular2020-12-21nan2020-12-21T04:00:00+00:004.0nan52898https://www.tvmaze.com/shows/52898/legend-of-yun-qianLegend of Yun QianScriptedChinese['Drama', 'Romance', 'History']Ended4.04.02020-12-212020-12-31nan67.0nan<p>The disciples of the Lingchuan Sect have guarded the Fans of Heaven and Earth for nearly a century. Mu Yun and Hua Yue are the only disciples of the sect that are left. The stubborn and disobedient Hua Yue unintentionally discovers that the Fan of Heaven possesses the power to travel through time. To escape being forced to study and practice martial arts by Mu Yun, Hua Yue travels to the future to have fun. Hundreds of years in the future she meets Xiao Qian who looks exactly like her. Secrets come to the surface, and adventures take place.</p>1.649956e+09https://api.tvmaze.com/episodes/2324428

Last rows

idurlnameseasonnumbertypeairdateairtimeairstampruntimesummary_embedded_show_id_embedded_show_url_embedded_show_name_embedded_show_type_embedded_show_language_embedded_show_genres_embedded_show_status_embedded_show_runtime_embedded_show_averageRuntime_embedded_show_premiered_embedded_show_ended_embedded_show_officialSite_embedded_show_weight_embedded_show_dvdCountry_embedded_show_summary_embedded_show_updated_links_self_href
721995452https://www.tvmaze.com/episodes/1995452/rooster-teeth-animated-adventures-2020-12-21-boulder-bsBoulder BS2020.050.0regular2020-12-21nan2020-12-21T17:00:00+00:002.0nan6147https://www.tvmaze.com/shows/6147/rooster-teeth-animated-adventuresRooster Teeth Animated AdventuresAnimationEnglish['Comedy']Running2.02.02011-09-28nanhttps://roosterteeth.com/series/rt-animated-adventures39.0nan<p>The stories from the popular Rooster Teeth Podcast now in animated form!</p>1.649262e+09https://api.tvmaze.com/episodes/1949331
731995453https://www.tvmaze.com/episodes/1995453/rooster-teeth-animated-adventures-2020-12-21-deer-stalkerDeer Stalker2020.051.0regular2020-12-21nan2020-12-21T17:00:00+00:002.0nan6147https://www.tvmaze.com/shows/6147/rooster-teeth-animated-adventuresRooster Teeth Animated AdventuresAnimationEnglish['Comedy']Running2.02.02011-09-28nanhttps://roosterteeth.com/series/rt-animated-adventures39.0nan<p>The stories from the popular Rooster Teeth Podcast now in animated form!</p>1.649262e+09https://api.tvmaze.com/episodes/1949332
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781988884https://www.tvmaze.com/episodes/1988884/upstart-crow-s03-special-lockdown-christmas-1603Lockdown Christmas 16033.0NaNsignificant_special2020-12-2121:002020-12-21T21:00:00+00:0030.0<p>The plague has hit London, and as Christmas approaches, Will and Kate are in wave fifteen of state-enforced home confinement together in Will's London lodgings.</p>9815https://www.tvmaze.com/shows/9815/upstart-crowUpstart CrowScriptedEnglish['Comedy']Ended30.030.02016-05-092020-12-21http://www.bbc.co.uk/programmes/b0959g2675.0nan<p>Comedy about William Shakespeare as he starts to make a name for himself in London while also trying to be a good husband and father for his family in Stratford-upon-Avon.</p>1.646749e+09https://api.tvmaze.com/episodes/1996786
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